Paper Info
Title
OpinionIt: a text mining system for cross-lingual opinion analysis
Abstract
Opinion mining focuses on extracting customers' opinions from the reviews and predicting their sentiment orientation. Reviewers usually praise a product in some aspects and bemoan it in other aspects. With the business globalization, it is very important for enterprises to extract the opinions toward different aspects and find out cross-lingual/cross-culture difference in opinions. Cross-lingual opinion mining is a very challenging task as amounts of opinions are written in different languages, and not well structured. Since people usually use different words to describe the same aspect in the reviews, product-feature (PF) categorization becomes very critical in cross-lingual opinion mining. Manual cross-lingual PF categorization is time consuming, and practically infeasible for the massive amount of data written in different languages. In order to effectively find out cross-lingual difference in opinions, we present an aspect-oriented opinion mining method with Cross-lingual Latent Semantic Association (CLaSA). We first construct CLaSA model to learn the cross-lingual latent semantic association among all the PFs from multi-dimension semantic clues in the review corpus. Then we employ CLaSA model to categorize all the multilingual PFs into semantic aspects, and summarize cross-lingual difference in opinions towards different aspects. Experimental results show that our method achieves better performance compared with the existing approaches. With CLaSA model, our text mining system OpinionIt can effectively discover cross-lingual difference in opinions.
Year
DOI
Venue
2010
10.1145/1871437.1871589
CIKM
Keywords
Field
DocType
different aspect,aspect-oriented opinion mining method,text mining system,opinion mining,different word,cross-lingual opinion analysis,cross-lingual opinion mining,cross-lingual latent semantic association,clasa model,cross-lingual difference,different language,manual cross-lingual pf categorization,aspect oriented,text mining
Data mining,Categorization,Cross lingual,Praise,Text mining,Semantic association,Information retrieval,Sentiment analysis,Computer science,Artificial intelligence,Natural language processing,Opinion analysis
Conference
Citations 
PageRank 
References 
10
0.56
26
Authors
5
Name
Order
Citations
PageRank
Honglei Guo126514.36
Huijia Zhu21396.97
Zhili guo326412.46
Xiaoxun Zhang433616.74
Zhong Su52282110.39